Overview

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Dataset statistics

Number of variables29
Number of observations784
Missing cells1842
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory418.0 KiB
Average record size in memory546.0 B

Variable types

Text2
DateTime3
Categorical2
Numeric22

Dataset

DescriptionDPHRU 013 - Metabolic study (cholesterol focus)
CreatorRP2 Clinical Data Harmonization Project
URLHEAT Research Projects

Variable descriptions

study_sourceSource study identifier
CD4 cell count (cells/µL)CD4+ T lymphocyte count - immune function indicator
HIV viral load (copies/mL)HIV RNA copies per mL - treatment efficacy marker
Albumin (g/dL)Serum albumin - liver function and nutritional status
primary_datePrimary date of measurement/visit
Age (at enrolment)Patient age at study enrollment

Alerts

BMI (kg/m²) is highly overall correlated with Waist circumference (cm) and 2 other fieldsHigh correlation
Diastolic blood pressure (mmHg) is highly overall correlated with Systolic blood pressure (mmHg) and 2 other fieldsHigh correlation
FASTING GLUCOSE is highly overall correlated with Fasting glucose (mmol/L)High correlation
FASTING HDL is highly overall correlated with FASTING TOTAL CHOLESTEROLHigh correlation
FASTING LDL is highly overall correlated with FASTING TOTAL CHOLESTEROL and 2 other fieldsHigh correlation
FASTING TOTAL CHOLESTEROL is highly overall correlated with FASTING HDL and 2 other fieldsHigh correlation
Fasting glucose (mmol/L) is highly overall correlated with FASTING GLUCOSEHigh correlation
Height is highly overall correlated with Height (m) and 1 other fieldsHigh correlation
Height (m) is highly overall correlated with Height and 1 other fieldsHigh correlation
LDL cholesterol (mg/dL) is highly overall correlated with FASTING LDL and 2 other fieldsHigh correlation
Systolic blood pressure (mmHg) is highly overall correlated with Diastolic blood pressure (mmHg) and 2 other fieldsHigh correlation
Total cholesterol (mg/dL) is highly overall correlated with FASTING LDL and 2 other fieldsHigh correlation
Waist circumference (cm) is highly overall correlated with BMI (kg/m²) and 2 other fieldsHigh correlation
Weight (kg) is highly overall correlated with BMI (kg/m²) and 2 other fieldsHigh correlation
diastolic blood pressure is highly overall correlated with Diastolic blood pressure (mmHg) and 2 other fieldsHigh correlation
month is highly overall correlated with seasonHigh correlation
original_record_index is highly overall correlated with LDL cholesterol (mg/dL)High correlation
season is highly overall correlated with monthHigh correlation
systolic blood pressure is highly overall correlated with Diastolic blood pressure (mmHg) and 2 other fieldsHigh correlation
weight is highly overall correlated with BMI (kg/m²) and 2 other fieldsHigh correlation
year is highly overall correlated with Height and 1 other fieldsHigh correlation
Age (at enrolment) has 14 (1.8%) missing valuesMissing
systolic blood pressure has 221 (28.2%) missing valuesMissing
diastolic blood pressure has 221 (28.2%) missing valuesMissing
weight has 221 (28.2%) missing valuesMissing
Height has 221 (28.2%) missing valuesMissing
FASTING GLUCOSE has 34 (4.3%) missing valuesMissing
FASTING HDL has 59 (7.5%) missing valuesMissing
FASTING LDL has 59 (7.5%) missing valuesMissing
FASTING TOTAL CHOLESTEROL has 60 (7.7%) missing valuesMissing
HDL cholesterol (mg/dL) has 27 (3.4%) missing valuesMissing
LDL cholesterol (mg/dL) has 27 (3.4%) missing valuesMissing
Total cholesterol (mg/dL) has 27 (3.4%) missing valuesMissing
Height (m) has 205 (26.1%) missing valuesMissing
Weight (kg) has 205 (26.1%) missing valuesMissing
Waist circumference (cm) has 205 (26.1%) missing valuesMissing
Fasting glucose (mmol/L) has 32 (4.1%) missing valuesMissing
original_record_index is uniformly distributedUniform
original_record_index has unique valuesUnique

Reproduction

Analysis started2025-11-11 10:35:47.348176
Analysis finished2025-11-11 10:41:46.058712
Duration5 minutes and 58.71 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Distinct247
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
2025-11-11T12:41:46.351996image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters13328
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)2.8%

Sample

1st rowHEAT_0D62D894DA8A
2nd rowHEAT_4D7DE69432FE
3rd rowHEAT_4D7DE69432FE
4th rowHEAT_4D7DE69432FE
5th rowHEAT_4D7DE69432FE
ValueCountFrequency (%)
heat_4d7de69432fe4
 
0.5%
heat_1b67a8e196f04
 
0.5%
heat_aa0e359abcba4
 
0.5%
heat_de071bff82684
 
0.5%
heat_6fbe09c8eed24
 
0.5%
heat_927cb51cfe434
 
0.5%
heat_1a8bbd77aac94
 
0.5%
heat_dc20966e386c4
 
0.5%
heat_ba33fc2535134
 
0.5%
heat_72b672d3a1824
 
0.5%
Other values (237)744
94.9%
2025-11-11T12:41:47.442029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E1451
 
10.9%
A1339
 
10.0%
H784
 
5.9%
T784
 
5.9%
_784
 
5.9%
0640
 
4.8%
3631
 
4.7%
F604
 
4.5%
D602
 
4.5%
1601
 
4.5%
Other values (9)5108
38.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)13328
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
E1451
 
10.9%
A1339
 
10.0%
H784
 
5.9%
T784
 
5.9%
_784
 
5.9%
0640
 
4.8%
3631
 
4.7%
F604
 
4.5%
D602
 
4.5%
1601
 
4.5%
Other values (9)5108
38.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)13328
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
E1451
 
10.9%
A1339
 
10.0%
H784
 
5.9%
T784
 
5.9%
_784
 
5.9%
0640
 
4.8%
3631
 
4.7%
F604
 
4.5%
D602
 
4.5%
1601
 
4.5%
Other values (9)5108
38.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)13328
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
E1451
 
10.9%
A1339
 
10.0%
H784
 
5.9%
T784
 
5.9%
_784
 
5.9%
0640
 
4.8%
3631
 
4.7%
F604
 
4.5%
D602
 
4.5%
1601
 
4.5%
Other values (9)5108
38.3%

primary_date
Date

Primary date of measurement/visit

Distinct232
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T12:41:47.943234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:41:49.315697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

year
Categorical

High correlation 

Distinct3
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size46.7 KiB
2011
462 
2012
195 
2013
127 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters3136
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2011
2nd row2011
3rd row2012
4th row2012
5th row2013

Common Values

ValueCountFrequency (%)
2011462
58.9%
2012195
24.9%
2013127
 
16.2%

Length

2025-11-11T12:41:50.704089image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:41:51.271640image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
2011462
58.9%
2012195
24.9%
2013127
 
16.2%

Most occurring characters

ValueCountFrequency (%)
11246
39.7%
2979
31.2%
0784
25.0%
3127
 
4.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)3136
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11246
39.7%
2979
31.2%
0784
25.0%
3127
 
4.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)3136
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11246
39.7%
2979
31.2%
0784
25.0%
3127
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)3136
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11246
39.7%
2979
31.2%
0784
25.0%
3127
 
4.0%

month
Real number (ℝ)

High correlation 

Distinct12
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1415816
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:41:51.927799image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q38
95-th percentile10
Maximum12
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.637117
Coefficient of variation (CV)0.51289996
Kurtosis-0.45367637
Mean5.1415816
Median Absolute Deviation (MAD)1
Skewness0.81252108
Sum4031
Variance6.9543863
MonotonicityNot monotonic
2025-11-11T12:41:52.424629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4179
22.8%
5144
18.4%
3133
17.0%
295
12.1%
878
9.9%
1046
 
5.9%
940
 
5.1%
1134
 
4.3%
626
 
3.3%
16
 
0.8%
Other values (2)3
 
0.4%
ValueCountFrequency (%)
16
 
0.8%
295
12.1%
3133
17.0%
4179
22.8%
5144
18.4%
626
 
3.3%
72
 
0.3%
878
9.9%
940
 
5.1%
1046
 
5.9%
ValueCountFrequency (%)
121
 
0.1%
1134
 
4.3%
1046
 
5.9%
940
 
5.1%
878
9.9%
72
 
0.3%
626
 
3.3%
5144
18.4%
4179
22.8%
3133
17.0%

season
Categorical

High correlation 

Distinct4
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size48.2 KiB
Autumn
456 
Spring
120 
Winter
106 
Summer
102 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters4704
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSummer
2nd rowAutumn
3rd rowSummer
4th rowAutumn
5th rowAutumn

Common Values

ValueCountFrequency (%)
Autumn456
58.2%
Spring120
 
15.3%
Winter106
 
13.5%
Summer102
 
13.0%

Length

2025-11-11T12:41:53.094502image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-11-11T12:41:53.714873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
autumn456
58.2%
spring120
 
15.3%
winter106
 
13.5%
summer102
 
13.0%

Most occurring characters

ValueCountFrequency (%)
u1014
21.6%
n682
14.5%
m660
14.0%
t562
11.9%
A456
9.7%
r328
 
7.0%
i226
 
4.8%
S222
 
4.7%
e208
 
4.4%
p120
 
2.6%
Other values (2)226
 
4.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)4704
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u1014
21.6%
n682
14.5%
m660
14.0%
t562
11.9%
A456
9.7%
r328
 
7.0%
i226
 
4.8%
S222
 
4.7%
e208
 
4.4%
p120
 
2.6%
Other values (2)226
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)4704
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u1014
21.6%
n682
14.5%
m660
14.0%
t562
11.9%
A456
9.7%
r328
 
7.0%
i226
 
4.8%
S222
 
4.7%
e208
 
4.4%
p120
 
2.6%
Other values (2)226
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)4704
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u1014
21.6%
n682
14.5%
m660
14.0%
t562
11.9%
A456
9.7%
r328
 
7.0%
i226
 
4.8%
S222
 
4.7%
e208
 
4.4%
p120
 
2.6%
Other values (2)226
 
4.8%

Age (at enrolment)
Real number (ℝ)

Missing 

Patient age at study enrollment

Distinct184
Distinct (%)23.9%
Missing14
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean33.596494
Minimum16
Maximum51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:41:54.578314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile22
Q128
median34
Q339
95-th percentile46
Maximum51
Range35
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.3995625
Coefficient of variation (CV)0.22024806
Kurtosis-0.78855897
Mean33.596494
Median Absolute Deviation (MAD)6
Skewness0.048568173
Sum25869.3
Variance54.753525
MonotonicityNot monotonic
2025-11-11T12:41:55.066778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4033
 
4.2%
3933
 
4.2%
3431
 
4.0%
3729
 
3.7%
3027
 
3.4%
3125
 
3.2%
3824
 
3.1%
4123
 
2.9%
3522
 
2.8%
2821
 
2.7%
Other values (174)502
64.0%
ValueCountFrequency (%)
161
 
0.1%
18.11
 
0.1%
18.81
 
0.1%
193
 
0.4%
19.31
 
0.1%
19.42
 
0.3%
19.51
 
0.1%
19.61
 
0.1%
209
1.1%
20.11
 
0.1%
ValueCountFrequency (%)
511
 
0.1%
504
 
0.5%
49.11
 
0.1%
496
0.8%
488
1.0%
47.91
 
0.1%
47.21
 
0.1%
4711
1.4%
46.61
 
0.1%
46.41
 
0.1%

systolic blood pressure
Real number (ℝ)

High correlation  Missing 

Distinct196
Distinct (%)34.8%
Missing221
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean119.35287
Minimum92
Maximum186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:41:55.654805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile98.366667
Q1108
median118
Q3127.16667
95-th percentile146.63333
Maximum186
Range94
Interquartile range (IQR)19.166667

Descriptive statistics

Standard deviation15.182279
Coefficient of variation (CV)0.12720497
Kurtosis1.0608157
Mean119.35287
Median Absolute Deviation (MAD)9.6666667
Skewness0.83903906
Sum67195.667
Variance230.50159
MonotonicityNot monotonic
2025-11-11T12:41:56.270612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12015
 
1.9%
12313
 
1.7%
11112
 
1.5%
12411
 
1.4%
12611
 
1.4%
11511
 
1.4%
11011
 
1.4%
10611
 
1.4%
11411
 
1.4%
10910
 
1.3%
Other values (186)447
57.0%
(Missing)221
28.2%
ValueCountFrequency (%)
922
0.3%
92.333333334
0.5%
932
0.3%
93.333333331
 
0.1%
93.666666671
 
0.1%
953
0.4%
95.333333331
 
0.1%
963
0.4%
96.333333333
0.4%
96.666666671
 
0.1%
ValueCountFrequency (%)
1861
0.1%
174.33333331
0.1%
1721
0.1%
171.66666671
0.1%
1681
0.1%
1651
0.1%
1641
0.1%
163.33333331
0.1%
1601
0.1%
1591
0.1%

diastolic blood pressure
Real number (ℝ)

High correlation  Missing 

Distinct183
Distinct (%)32.5%
Missing221
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean79.904085
Minimum40
Maximum129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:41:56.888012image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile63
Q172
median78
Q387
95-th percentile102
Maximum129
Range89
Interquartile range (IQR)15

Descriptive statistics

Standard deviation12.282504
Coefficient of variation (CV)0.15371559
Kurtosis1.1453442
Mean79.904085
Median Absolute Deviation (MAD)7
Skewness0.70003203
Sum44986
Variance150.8599
MonotonicityNot monotonic
2025-11-11T12:41:57.418388image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7820
 
2.6%
7518
 
2.3%
7416
 
2.0%
6914
 
1.8%
7714
 
1.8%
7213
 
1.7%
7112
 
1.5%
8011
 
1.4%
8211
 
1.4%
8811
 
1.4%
Other values (173)423
54.0%
(Missing)221
28.2%
ValueCountFrequency (%)
401
 
0.1%
43.666666671
 
0.1%
541
 
0.1%
571
 
0.1%
57.333333331
 
0.1%
58.333333332
 
0.3%
58.333333331
 
0.1%
595
0.6%
59.666666671
 
0.1%
603
0.4%
ValueCountFrequency (%)
1292
0.3%
125.33333331
0.1%
1221
0.1%
119.66666671
0.1%
1171
0.1%
114.66666671
0.1%
113.33333331
0.1%
1131
0.1%
1082
0.3%
107.66666671
0.1%

weight
Real number (ℝ)

High correlation  Missing 

Distinct360
Distinct (%)63.9%
Missing221
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean69.787744
Minimum35.1
Maximum140.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:41:57.984192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.1
5-th percentile47.61
Q157.9
median67.2
Q378.4
95-th percentile102.99
Maximum140.5
Range105.4
Interquartile range (IQR)20.5

Descriptive statistics

Standard deviation16.938157
Coefficient of variation (CV)0.24270962
Kurtosis1.3539238
Mean69.787744
Median Absolute Deviation (MAD)10
Skewness0.98611018
Sum39290.5
Variance286.90115
MonotonicityNot monotonic
2025-11-11T12:41:58.595305image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
72.35
 
0.6%
65.45
 
0.6%
544
 
0.5%
53.74
 
0.5%
55.14
 
0.5%
61.84
 
0.5%
65.64
 
0.5%
76.64
 
0.5%
70.94
 
0.5%
59.64
 
0.5%
Other values (350)521
66.5%
(Missing)221
28.2%
ValueCountFrequency (%)
35.11
0.1%
35.81
0.1%
36.41
0.1%
39.81
0.1%
41.61
0.1%
41.81
0.1%
421
0.1%
42.12
0.3%
42.51
0.1%
43.61
0.1%
ValueCountFrequency (%)
140.51
0.1%
135.21
0.1%
133.81
0.1%
130.61
0.1%
129.11
0.1%
121.91
0.1%
1181
0.1%
116.31
0.1%
115.81
0.1%
114.71
0.1%

Height
Real number (ℝ)

High correlation  Missing 

Distinct222
Distinct (%)39.4%
Missing221
Missing (%)28.2%
Infinite0
Infinite (%)0.0%
Mean1.2672966
Minimum0.139
Maximum1.785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:41:59.185936image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.139
5-th percentile0.155
Q11.4845
median1.567
Q31.61
95-th percentile1.6719
Maximum1.785
Range1.646
Interquartile range (IQR)0.1255

Descriptive statistics

Standard deviation0.59809282
Coefficient of variation (CV)0.47194382
Kurtosis-0.26167678
Mean1.2672966
Median Absolute Deviation (MAD)0.051
Skewness-1.3030932
Sum713.488
Variance0.35771502
MonotonicityNot monotonic
2025-11-11T12:41:59.802573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15515
 
1.9%
0.15814
 
1.8%
0.1599
 
1.1%
0.1579
 
1.1%
0.1618
 
1.0%
0.168
 
1.0%
0.1637
 
0.9%
1.617
 
0.9%
1.5847
 
0.9%
1.5887
 
0.9%
Other values (212)472
60.2%
(Missing)221
28.2%
ValueCountFrequency (%)
0.1391
 
0.1%
0.141
 
0.1%
0.1471
 
0.1%
0.1481
 
0.1%
0.1492
 
0.3%
0.155
 
0.6%
0.1523
 
0.4%
0.1537
0.9%
0.1546
 
0.8%
0.15515
1.9%
ValueCountFrequency (%)
1.7851
0.1%
1.781
0.1%
1.7621
0.1%
1.7591
0.1%
1.7571
0.1%
1.7331
0.1%
1.7171
0.1%
1.7151
0.1%
1.711
0.1%
1.7081
0.1%
Distinct247
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Memory size49.0 KiB
2025-11-11T12:42:00.922970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters5488
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)2.8%

Sample

1st rowWBS 001
2nd rowWBS 003
3rd rowWBS 003
4th rowWBS 003
5th rowWBS 003
ValueCountFrequency (%)
wbs784
50.0%
0034
 
0.3%
0044
 
0.3%
0104
 
0.3%
0084
 
0.3%
0174
 
0.3%
0144
 
0.3%
0114
 
0.3%
0284
 
0.3%
0274
 
0.3%
Other values (238)748
47.7%
2025-11-11T12:42:02.192045image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
W784
14.3%
B784
14.3%
S784
14.3%
784
14.3%
2426
7.8%
0425
7.7%
1419
7.6%
3220
 
4.0%
5151
 
2.8%
7146
 
2.7%
Other values (4)565
10.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)5488
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
W784
14.3%
B784
14.3%
S784
14.3%
784
14.3%
2426
7.8%
0425
7.7%
1419
7.6%
3220
 
4.0%
5151
 
2.8%
7146
 
2.7%
Other values (4)565
10.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)5488
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
W784
14.3%
B784
14.3%
S784
14.3%
784
14.3%
2426
7.8%
0425
7.7%
1419
7.6%
3220
 
4.0%
5151
 
2.8%
7146
 
2.7%
Other values (4)565
10.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)5488
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
W784
14.3%
B784
14.3%
S784
14.3%
784
14.3%
2426
7.8%
0425
7.7%
1419
7.6%
3220
 
4.0%
5151
 
2.8%
7146
 
2.7%
Other values (4)565
10.3%

original_record_index
Real number (ℝ)

High correlation  Uniform  Unique 

Distinct784
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean391.5
Minimum0
Maximum783
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:02.460711image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile39.15
Q1195.75
median391.5
Q3587.25
95-th percentile743.85
Maximum783
Range783
Interquartile range (IQR)391.5

Descriptive statistics

Standard deviation226.4656
Coefficient of variation (CV)0.57845619
Kurtosis-1.2
Mean391.5
Median Absolute Deviation (MAD)196
Skewness0
Sum306936
Variance51286.667
MonotonicityStrictly increasing
2025-11-11T12:42:03.111015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7831
 
0.1%
01
 
0.1%
11
 
0.1%
21
 
0.1%
31
 
0.1%
41
 
0.1%
51
 
0.1%
61
 
0.1%
71
 
0.1%
81
 
0.1%
Other values (774)774
98.7%
ValueCountFrequency (%)
01
0.1%
11
0.1%
21
0.1%
31
0.1%
41
0.1%
51
0.1%
61
0.1%
71
0.1%
81
0.1%
91
0.1%
ValueCountFrequency (%)
7831
0.1%
7821
0.1%
7811
0.1%
7801
0.1%
7791
0.1%
7781
0.1%
7771
0.1%
7761
0.1%
7751
0.1%
7741
0.1%

date
Date

Distinct232
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T12:42:04.084149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:42:05.461390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

FASTING GLUCOSE
Real number (ℝ)

High correlation  Missing 

Distinct278
Distinct (%)37.1%
Missing34
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean4.93656
Minimum0.95
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:06.697019image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile3.3545
Q14.51
median4.94
Q35.42
95-th percentile6.15
Maximum15
Range14.05
Interquartile range (IQR)0.91

Descriptive statistics

Standard deviation0.95029735
Coefficient of variation (CV)0.19250193
Kurtosis19.372052
Mean4.93656
Median Absolute Deviation (MAD)0.46
Skewness1.4967374
Sum3702.42
Variance0.90306505
MonotonicityNot monotonic
2025-11-11T12:42:07.128235image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.759
 
1.1%
5.219
 
1.1%
5.428
 
1.0%
4.938
 
1.0%
4.828
 
1.0%
4.78
 
1.0%
5.248
 
1.0%
4.737
 
0.9%
5.177
 
0.9%
4.577
 
0.9%
Other values (268)671
85.6%
(Missing)34
 
4.3%
ValueCountFrequency (%)
0.951
0.1%
1.121
0.1%
1.371
0.1%
1.471
0.1%
2.021
0.1%
2.041
0.1%
2.211
0.1%
2.221
0.1%
2.261
0.1%
2.552
0.3%
ValueCountFrequency (%)
151
0.1%
9.911
0.1%
9.671
0.1%
8.241
0.1%
7.971
0.1%
7.621
0.1%
7.611
0.1%
7.421
0.1%
7.271
0.1%
7.061
0.1%

FASTING HDL
Real number (ℝ)

High correlation  Missing 

Distinct174
Distinct (%)24.0%
Missing59
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean1.1195586
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:07.671403image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.512
Q10.84
median1.07
Q31.36
95-th percentile1.88
Maximum3.7
Range3.42
Interquartile range (IQR)0.52

Descriptive statistics

Standard deviation0.44014039
Coefficient of variation (CV)0.39313742
Kurtosis4.5641963
Mean1.1195586
Median Absolute Deviation (MAD)0.26
Skewness1.3040858
Sum811.68
Variance0.19372356
MonotonicityNot monotonic
2025-11-11T12:42:08.158396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.814
 
1.8%
1.0414
 
1.8%
0.9313
 
1.7%
0.8513
 
1.7%
1.113
 
1.7%
1.1811
 
1.4%
111
 
1.4%
0.9511
 
1.4%
0.9410
 
1.3%
0.8410
 
1.3%
Other values (164)605
77.2%
(Missing)59
 
7.5%
ValueCountFrequency (%)
0.281
0.1%
0.321
0.1%
0.332
0.3%
0.342
0.3%
0.351
0.1%
0.362
0.3%
0.372
0.3%
0.391
0.1%
0.42
0.3%
0.412
0.3%
ValueCountFrequency (%)
3.73
0.4%
2.81
 
0.1%
2.532
0.3%
2.491
 
0.1%
2.441
 
0.1%
2.311
 
0.1%
2.31
 
0.1%
2.291
 
0.1%
2.242
0.3%
2.231
 
0.1%

FASTING LDL
Real number (ℝ)

High correlation  Missing 

Distinct262
Distinct (%)36.1%
Missing59
Missing (%)7.5%
Infinite0
Infinite (%)0.0%
Mean1.6814897
Minimum0
Maximum6.04
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:08.751313image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.672
Q11.11
median1.54
Q32.08
95-th percentile3.18
Maximum6.04
Range6.04
Interquartile range (IQR)0.97

Descriptive statistics

Standard deviation0.77228422
Coefficient of variation (CV)0.45928574
Kurtosis1.7596989
Mean1.6814897
Median Absolute Deviation (MAD)0.48
Skewness1.052318
Sum1219.08
Variance0.59642292
MonotonicityNot monotonic
2025-11-11T12:42:09.145418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.329
 
1.1%
1.019
 
1.1%
1.129
 
1.1%
1.298
 
1.0%
1.378
 
1.0%
1.767
 
0.9%
1.257
 
0.9%
1.057
 
0.9%
1.087
 
0.9%
1.657
 
0.9%
Other values (252)647
82.5%
(Missing)59
 
7.5%
ValueCountFrequency (%)
01
 
0.1%
0.331
 
0.1%
0.391
 
0.1%
0.422
 
0.3%
0.451
 
0.1%
0.461
 
0.1%
0.471
 
0.1%
0.53
0.4%
0.555
0.6%
0.564
0.5%
ValueCountFrequency (%)
6.041
0.1%
4.411
0.1%
4.281
0.1%
4.252
0.3%
4.191
0.1%
4.131
0.1%
3.971
0.1%
3.941
0.1%
3.891
0.1%
3.871
0.1%

FASTING TOTAL CHOLESTEROL
Real number (ℝ)

High correlation  Missing 

Distinct332
Distinct (%)45.9%
Missing60
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean4.1242818
Minimum1.12
Maximum10.48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:09.683703image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.12
5-th percentile2.55
Q13.39
median4
Q34.81
95-th percentile6.0085
Maximum10.48
Range9.36
Interquartile range (IQR)1.42

Descriptive statistics

Standard deviation1.1561844
Coefficient of variation (CV)0.28033593
Kurtosis3.348478
Mean4.1242818
Median Absolute Deviation (MAD)0.705
Skewness1.00603
Sum2985.98
Variance1.3367623
MonotonicityNot monotonic
2025-11-11T12:42:10.349697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
410
 
1.3%
4.119
 
1.1%
3.627
 
0.9%
3.897
 
0.9%
3.486
 
0.8%
4.576
 
0.8%
4.936
 
0.8%
3.686
 
0.8%
4.476
 
0.8%
4.235
 
0.6%
Other values (322)656
83.7%
(Missing)60
 
7.7%
ValueCountFrequency (%)
1.121
0.1%
1.221
0.1%
1.291
0.1%
1.381
0.1%
1.541
0.1%
1.591
0.1%
1.82
0.3%
1.851
0.1%
2.012
0.3%
2.061
0.1%
ValueCountFrequency (%)
10.481
0.1%
10.291
0.1%
9.282
0.3%
9.041
0.1%
8.651
0.1%
7.71
0.1%
7.591
0.1%
7.31
0.1%
7.281
0.1%
6.821
0.1%
Distinct232
Distinct (%)29.6%
Missing0
Missing (%)0.0%
Memory size12.2 KiB
Minimum2011-02-10 00:00:00
Maximum2013-06-19 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-11-11T12:42:11.012328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:42:12.219342image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

HDL cholesterol (mg/dL)
Real number (ℝ)

Missing 

Distinct112
Distinct (%)14.8%
Missing27
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean1.0279921
Minimum0.28
Maximum3.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:13.296318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.28
5-th percentile0.58
Q10.79
median0.96
Q31.17
95-th percentile1.612
Maximum3.7
Range3.42
Interquartile range (IQR)0.38

Descriptive statistics

Standard deviation0.40729906
Coefficient of variation (CV)0.39620837
Kurtosis14.338385
Mean1.0279921
Median Absolute Deviation (MAD)0.19
Skewness2.7299803
Sum778.19
Variance0.16589252
MonotonicityNot monotonic
2025-11-11T12:42:13.707096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.127
 
3.4%
0.8521
 
2.7%
0.7321
 
2.7%
0.821
 
2.7%
0.9519
 
2.4%
1.0418
 
2.3%
1.0817
 
2.2%
0.8717
 
2.2%
0.9315
 
1.9%
0.6715
 
1.9%
Other values (102)566
72.2%
(Missing)27
 
3.4%
ValueCountFrequency (%)
0.281
 
0.1%
0.324
0.5%
0.344
0.5%
0.393
0.4%
0.455
0.6%
0.513
0.4%
0.524
0.5%
0.531
 
0.1%
0.541
 
0.1%
0.577
0.9%
ValueCountFrequency (%)
3.76
0.8%
2.83
 
0.4%
1.9310
1.3%
1.891
 
0.1%
1.883
 
0.4%
1.771
 
0.1%
1.764
 
0.5%
1.754
 
0.5%
1.681
 
0.1%
1.661
 
0.1%

LDL cholesterol (mg/dL)
Real number (ℝ)

High correlation  Missing 

Distinct160
Distinct (%)21.1%
Missing27
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean1.7226684
Minimum0
Maximum4.28
Zeros3
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:14.222753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.67
Q11.03
median1.52
Q32.16
95-th percentile3.432
Maximum4.28
Range4.28
Interquartile range (IQR)1.13

Descriptive statistics

Standard deviation0.86534729
Coefficient of variation (CV)0.50232957
Kurtosis0.15127231
Mean1.7226684
Median Absolute Deviation (MAD)0.53
Skewness0.84048185
Sum1304.06
Variance0.74882594
MonotonicityNot monotonic
2025-11-11T12:42:14.565825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.2918
 
2.3%
2.1515
 
1.9%
1.0115
 
1.9%
1.3714
 
1.8%
0.8214
 
1.8%
1.4612
 
1.5%
1.8410
 
1.3%
2.6510
 
1.3%
110
 
1.3%
1.0310
 
1.3%
Other values (150)629
80.2%
(Missing)27
 
3.4%
ValueCountFrequency (%)
03
0.4%
0.331
 
0.1%
0.421
 
0.1%
0.453
0.4%
0.463
0.4%
0.474
0.5%
0.54
0.5%
0.557
0.9%
0.563
0.4%
0.594
0.5%
ValueCountFrequency (%)
4.284
0.5%
4.258
1.0%
3.874
0.5%
3.722
 
0.3%
3.714
0.5%
3.564
0.5%
3.54
0.5%
3.484
0.5%
3.444
0.5%
3.432
 
0.3%

Total cholesterol (mg/dL)
Real number (ℝ)

High correlation  Missing 

Distinct161
Distinct (%)21.3%
Missing27
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean3.9215059
Minimum1.85
Maximum7.59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:14.970648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.85
5-th percentile2.59
Q13.29
median3.82
Q34.49
95-th percentile5.41
Maximum7.59
Range5.74
Interquartile range (IQR)1.2

Descriptive statistics

Standard deviation0.91035931
Coefficient of variation (CV)0.23214534
Kurtosis0.61950783
Mean3.9215059
Median Absolute Deviation (MAD)0.6
Skewness0.58455733
Sum2968.58
Variance0.82875408
MonotonicityNot monotonic
2025-11-11T12:42:15.316537image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9317
 
2.2%
2.7714
 
1.8%
4.3313
 
1.7%
2.6612
 
1.5%
3.3511
 
1.4%
3.5210
 
1.3%
3.710
 
1.3%
4.1110
 
1.3%
4.3710
 
1.3%
3.7910
 
1.3%
Other values (151)640
81.6%
(Missing)27
 
3.4%
ValueCountFrequency (%)
1.851
 
0.1%
2.074
 
0.5%
2.264
 
0.5%
2.4910
1.3%
2.53
 
0.4%
2.534
 
0.5%
2.554
 
0.5%
2.564
 
0.5%
2.598
1.0%
2.63
 
0.4%
ValueCountFrequency (%)
7.594
0.5%
6.563
0.4%
6.44
0.5%
5.874
0.5%
5.684
0.5%
5.652
 
0.3%
5.67
0.9%
5.543
0.4%
5.514
0.5%
5.52
 
0.3%

BMI (kg/m²)
Real number (ℝ)

High correlation 

Distinct248
Distinct (%)31.7%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean27.86986
Minimum15.1
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:15.773769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum15.1
5-th percentile19.2
Q122.9
median26.7
Q331.55
95-th percentile40.58
Maximum57
Range41.9
Interquartile range (IQR)8.65

Descriptive statistics

Standard deviation6.724033
Coefficient of variation (CV)0.24126541
Kurtosis1.639969
Mean27.86986
Median Absolute Deviation (MAD)4.2
Skewness1.0713027
Sum21822.1
Variance45.21262
MonotonicityNot monotonic
2025-11-11T12:42:16.177168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26.311
 
1.4%
21.810
 
1.3%
22.910
 
1.3%
2510
 
1.3%
21.510
 
1.3%
32.39
 
1.1%
26.79
 
1.1%
27.48
 
1.0%
25.88
 
1.0%
21.28
 
1.0%
Other values (238)690
88.0%
ValueCountFrequency (%)
15.11
0.1%
15.31
0.1%
161
0.1%
16.11
0.1%
16.61
0.1%
16.81
0.1%
16.91
0.1%
17.11
0.1%
17.21
0.1%
17.31
0.1%
ValueCountFrequency (%)
571
 
0.1%
56.11
 
0.1%
54.91
 
0.1%
54.31
 
0.1%
50.71
 
0.1%
50.42
0.3%
50.11
 
0.1%
49.83
0.4%
491
 
0.1%
46.42
0.3%

Height (m)
Real number (ℝ)

High correlation  Missing 

Distinct222
Distinct (%)38.3%
Missing205
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean1.2761693
Minimum0.139
Maximum1.785
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:16.650647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.139
5-th percentile0.155
Q11.491
median1.567
Q31.61
95-th percentile1.6721
Maximum1.785
Range1.646
Interquartile range (IQR)0.119

Descriptive statistics

Standard deviation0.592174
Coefficient of variation (CV)0.46402466
Kurtosis-0.14690139
Mean1.2761693
Median Absolute Deviation (MAD)0.05
Skewness-1.345551
Sum738.902
Variance0.35067005
MonotonicityNot monotonic
2025-11-11T12:42:17.221338image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.15515
 
1.9%
0.15814
 
1.8%
0.1599
 
1.1%
0.1579
 
1.1%
1.5889
 
1.1%
0.168
 
1.0%
0.1618
 
1.0%
1.618
 
1.0%
1.5847
 
0.9%
0.1637
 
0.9%
Other values (212)485
61.9%
(Missing)205
26.1%
ValueCountFrequency (%)
0.1391
 
0.1%
0.141
 
0.1%
0.1471
 
0.1%
0.1481
 
0.1%
0.1492
 
0.3%
0.155
 
0.6%
0.1523
 
0.4%
0.1537
0.9%
0.1546
 
0.8%
0.15515
1.9%
ValueCountFrequency (%)
1.7851
0.1%
1.781
0.1%
1.7621
0.1%
1.7591
0.1%
1.7571
0.1%
1.7331
0.1%
1.7171
0.1%
1.7151
0.1%
1.711
0.1%
1.7082
0.3%

Weight (kg)
Real number (ℝ)

High correlation  Missing 

Distinct360
Distinct (%)62.2%
Missing205
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean69.846805
Minimum35.1
Maximum140.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:17.904542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum35.1
5-th percentile47.57
Q157.9
median67.2
Q378.5
95-th percentile103
Maximum140.5
Range105.4
Interquartile range (IQR)20.6

Descriptive statistics

Standard deviation17.051946
Coefficient of variation (CV)0.24413351
Kurtosis1.4115677
Mean69.846805
Median Absolute Deviation (MAD)10
Skewness1.0019699
Sum40441.3
Variance290.76886
MonotonicityNot monotonic
2025-11-11T12:42:18.479038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
65.45
 
0.6%
72.35
 
0.6%
76.84
 
0.5%
734
 
0.5%
53.74
 
0.5%
76.64
 
0.5%
69.44
 
0.5%
544
 
0.5%
70.94
 
0.5%
59.64
 
0.5%
Other values (350)537
68.5%
(Missing)205
 
26.1%
ValueCountFrequency (%)
35.11
0.1%
35.81
0.1%
36.41
0.1%
39.81
0.1%
41.61
0.1%
41.81
0.1%
421
0.1%
42.12
0.3%
42.51
0.1%
43.61
0.1%
ValueCountFrequency (%)
140.51
0.1%
135.21
0.1%
133.81
0.1%
130.62
0.3%
129.11
0.1%
121.91
0.1%
1181
0.1%
116.31
0.1%
115.81
0.1%
114.71
0.1%

Waist circumference (cm)
Real number (ℝ)

High correlation  Missing 

Distinct115
Distinct (%)19.9%
Missing205
Missing (%)26.1%
Infinite0
Infinite (%)0.0%
Mean894.1658
Minimum29
Maximum9150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:19.086785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum29
5-th percentile675
Q1780
median865
Q3970
95-th percentile1170
Maximum9150
Range9121
Interquartile range (IQR)190

Descriptive statistics

Standard deviation378.81667
Coefficient of variation (CV)0.42365372
Kurtosis391.5718
Mean894.1658
Median Absolute Deviation (MAD)95
Skewness17.961673
Sum517722
Variance143502.07
MonotonicityNot monotonic
2025-11-11T12:42:19.748589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
87023
 
2.9%
81019
 
2.4%
85019
 
2.4%
78019
 
2.4%
89018
 
2.3%
86017
 
2.2%
79016
 
2.0%
74016
 
2.0%
76015
 
1.9%
80013
 
1.7%
Other values (105)404
51.5%
(Missing)205
26.1%
ValueCountFrequency (%)
291
 
0.1%
811
 
0.1%
1081
 
0.1%
5901
 
0.1%
6102
 
0.3%
6201
 
0.1%
6304
0.5%
6401
 
0.1%
6502
 
0.3%
6605
0.6%
ValueCountFrequency (%)
91501
0.1%
15101
0.1%
14501
0.1%
14352
0.3%
14001
0.1%
13301
0.1%
13101
0.1%
13001
0.1%
12951
0.1%
12802
0.3%

Fasting glucose (mmol/L)
Real number (ℝ)

High correlation  Missing 

Distinct276
Distinct (%)36.7%
Missing32
Missing (%)4.1%
Infinite0
Infinite (%)0.0%
Mean4.9239362
Minimum0.95
Maximum15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:20.342473image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile3.332
Q14.4975
median4.93
Q35.41
95-th percentile6.1145
Maximum15
Range14.05
Interquartile range (IQR)0.9125

Descriptive statistics

Standard deviation0.9504881
Coefficient of variation (CV)0.1930342
Kurtosis19.390246
Mean4.9239362
Median Absolute Deviation (MAD)0.45
Skewness1.5000594
Sum3702.8
Variance0.90342763
MonotonicityNot monotonic
2025-11-11T12:42:20.801805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5.2110
 
1.3%
4.759
 
1.1%
4.939
 
1.1%
5.248
 
1.0%
4.78
 
1.0%
4.828
 
1.0%
5.428
 
1.0%
4.577
 
0.9%
4.737
 
0.9%
5.177
 
0.9%
Other values (266)671
85.6%
(Missing)32
 
4.1%
ValueCountFrequency (%)
0.951
0.1%
1.121
0.1%
1.371
0.1%
1.471
0.1%
2.021
0.1%
2.041
0.1%
2.211
0.1%
2.221
0.1%
2.261
0.1%
2.552
0.3%
ValueCountFrequency (%)
151
0.1%
9.911
0.1%
9.671
0.1%
8.241
0.1%
7.971
0.1%
7.621
0.1%
7.611
0.1%
7.421
0.1%
7.271
0.1%
7.061
0.1%

Systolic blood pressure (mmHg)
Real number (ℝ)

High correlation 

Distinct63
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.59056
Minimum92
Maximum172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:21.367066image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum92
5-th percentile102
Q1111
median119
Q3129
95-th percentile151
Maximum172
Range80
Interquartile range (IQR)18

Descriptive statistics

Standard deviation15.018999
Coefficient of variation (CV)0.12352109
Kurtosis0.67639476
Mean121.59056
Median Absolute Deviation (MAD)9
Skewness0.8410264
Sum95327
Variance225.57033
MonotonicityNot monotonic
2025-11-11T12:42:21.969947image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12432
 
4.1%
11131
 
4.0%
11630
 
3.8%
10829
 
3.7%
12026
 
3.3%
11025
 
3.2%
11324
 
3.1%
12624
 
3.1%
12724
 
3.1%
11924
 
3.1%
Other values (53)515
65.7%
ValueCountFrequency (%)
924
 
0.5%
957
 
0.9%
965
 
0.6%
974
 
0.5%
982
 
0.3%
994
 
0.5%
1005
 
0.6%
10219
2.4%
10316
2.0%
1044
 
0.5%
ValueCountFrequency (%)
1724
0.5%
1684
0.5%
1653
 
0.4%
1644
0.5%
1594
0.5%
1578
1.0%
1553
 
0.4%
1534
0.5%
1522
 
0.3%
1516
0.8%

Diastolic blood pressure (mmHg)
Real number (ℝ)

High correlation 

Distinct51
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.456633
Minimum40
Maximum122
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:22.620081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile62
Q171
median78
Q387
95-th percentile101.85
Maximum122
Range82
Interquartile range (IQR)16

Descriptive statistics

Standard deviation12.000773
Coefficient of variation (CV)0.15103551
Kurtosis0.69715107
Mean79.456633
Median Absolute Deviation (MAD)8
Skewness0.5707229
Sum62294
Variance144.01855
MonotonicityNot monotonic
2025-11-11T12:42:23.133493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7545
 
5.7%
7844
 
5.6%
7436
 
4.6%
6935
 
4.5%
7132
 
4.1%
8430
 
3.8%
7229
 
3.7%
7727
 
3.4%
7626
 
3.3%
8724
 
3.1%
Other values (41)456
58.2%
ValueCountFrequency (%)
403
 
0.4%
5914
1.8%
605
 
0.6%
618
1.0%
6211
1.4%
637
0.9%
6416
2.0%
6514
1.8%
6614
1.8%
6715
1.9%
ValueCountFrequency (%)
1224
 
0.5%
1173
 
0.4%
1134
 
0.5%
1082
 
0.3%
1074
 
0.5%
1063
 
0.4%
10312
1.5%
1028
1.0%
10115
1.9%
1006
 
0.8%

Fasting glucose (mg/dL)
Real number (ℝ)

Distinct141
Distinct (%)18.1%
Missing3
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean4.841306
Minimum0.95
Maximum9.91
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.2 KiB
2025-11-11T12:42:23.673862image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.95
5-th percentile3.69
Q14.48
median4.82
Q35.22
95-th percentile5.79
Maximum9.91
Range8.96
Interquartile range (IQR)0.74

Descriptive statistics

Standard deviation0.74383851
Coefficient of variation (CV)0.15364418
Kurtosis12.381801
Mean4.841306
Median Absolute Deviation (MAD)0.36
Skewness1.2009858
Sum3781.06
Variance0.55329573
MonotonicityNot monotonic
2025-11-11T12:42:24.099315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.4515
 
1.9%
4.7315
 
1.9%
4.4814
 
1.8%
5.2114
 
1.8%
4.713
 
1.7%
4.5512
 
1.5%
5.1712
 
1.5%
4.9312
 
1.5%
4.2911
 
1.4%
4.5711
 
1.4%
Other values (131)652
83.2%
ValueCountFrequency (%)
0.952
 
0.3%
3.023
 
0.4%
3.033
 
0.4%
3.073
 
0.4%
3.112
 
0.3%
3.317
0.9%
3.354
0.5%
3.434
0.5%
3.558
1.0%
3.652
 
0.3%
ValueCountFrequency (%)
9.914
0.5%
7.623
 
0.4%
6.654
0.5%
6.024
0.5%
5.984
0.5%
5.933
 
0.4%
5.884
0.5%
5.868
1.0%
5.844
0.5%
5.798
1.0%

Interactions

2025-11-11T12:41:14.337655image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:35:49.509987image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:01.051853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:15.569289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:29.891160image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:44.088955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:56.939362image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:12.987696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:32.031016image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:46.636011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:02.781868image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:15.047327image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:30.667911image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:48.395425image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:03.393525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:19.111767image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:32.686871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:49.442430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:04.124440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:20.985967image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:36.769224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:56.706260image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:41:14.883318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:35:49.809394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:01.594038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:16.113774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:30.412997image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:44.533802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:57.553288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:13.715985image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:32.557825image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:47.233895image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:03.221076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:15.538486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:31.351501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:48.893394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:03.943496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:19.615528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:33.301634image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:49.974777image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:04.707262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:21.556037image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:37.543480image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:57.405535image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:41:15.598169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:35:50.373144image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:02.178055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:16.782432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:31.052572image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:45.101606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:58.315524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:14.594887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:33.252568image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:48.014880image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:03.823315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:16.277507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:32.226918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:49.598625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:04.676162image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:20.239072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-11-11T12:40:54.896005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:41:12.836232image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:41:29.509380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:00.581192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:14.926431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:29.259903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:43.463354image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:36:56.333220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:12.273203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:31.174728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:37:45.991087image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:02.061616image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:14.505495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:29.946118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:38:47.565051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:02.710789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:18.378940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:32.114718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:39:48.689190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:03.491099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:20.245843image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:36.094284image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:40:55.850018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-11-11T12:41:13.571735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-11-11T12:42:24.541434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Age (at enrolment)BMI (kg/m²)Diastolic blood pressure (mmHg)FASTING GLUCOSEFASTING HDLFASTING LDLFASTING TOTAL CHOLESTEROLFasting glucose (mg/dL)Fasting glucose (mmol/L)HDL cholesterol (mg/dL)HeightHeight (m)LDL cholesterol (mg/dL)Systolic blood pressure (mmHg)Total cholesterol (mg/dL)Waist circumference (cm)Weight (kg)diastolic blood pressuremonthoriginal_record_indexseasonsystolic blood pressureweightyear
Age (at enrolment)1.0000.2300.3260.1650.0160.1540.1520.1750.159-0.113-0.138-0.1300.1210.2180.1630.2900.2210.3010.0020.0140.0380.1510.2180.094
BMI (kg/m²)0.2301.0000.3270.1240.0140.1020.0920.1550.1280.072-0.117-0.1170.1600.2930.1420.8980.9380.387-0.025-0.0890.0000.2920.9370.000
Diastolic blood pressure (mmHg)0.3260.3271.0000.0260.0150.0150.1350.1140.025-0.090-0.033-0.039-0.0190.7940.0860.3160.3270.8470.0500.1100.0020.6680.3230.000
FASTING GLUCOSE0.1650.1240.0261.0000.0050.032-0.0720.4560.988-0.041-0.137-0.1350.0390.017-0.0600.1860.1430.074-0.1590.0110.186-0.0010.1330.178
FASTING HDL0.0160.0140.0150.0051.0000.2670.5080.0470.0070.379-0.037-0.0360.118-0.0070.269-0.0040.012-0.023-0.1000.0030.150-0.0230.0210.158
FASTING LDL0.1540.1020.0150.0320.2671.0000.5670.0170.0280.268-0.127-0.1170.6310.0090.6010.0840.0890.031-0.214-0.1970.0940.0070.0930.106
FASTING TOTAL CHOLESTEROL0.1520.0920.135-0.0720.5080.5671.000-0.033-0.0660.217-0.013-0.0150.4070.0900.6830.1020.0780.1140.0080.0480.1420.0870.0840.100
Fasting glucose (mg/dL)0.1750.1550.1140.4560.0470.017-0.0331.0000.4680.042-0.021-0.0220.0950.047-0.0280.1730.1450.0690.0420.0230.0530.0210.1300.000
Fasting glucose (mmol/L)0.1590.1280.0250.9880.0070.028-0.0660.4681.000-0.032-0.137-0.1370.0460.023-0.0600.1900.1480.074-0.1580.0080.182-0.0010.1330.186
HDL cholesterol (mg/dL)-0.1130.072-0.090-0.0410.3790.2680.2170.042-0.0321.000-0.047-0.0420.483-0.0160.4870.0230.035-0.070-0.174-0.4180.151-0.0340.0490.000
Height-0.138-0.117-0.033-0.137-0.037-0.127-0.013-0.021-0.137-0.0471.0001.000-0.056-0.021-0.0800.0130.082-0.040-0.1860.0270.1740.1100.0820.701
Height (m)-0.130-0.117-0.039-0.135-0.036-0.117-0.015-0.022-0.137-0.0421.0001.000-0.053-0.026-0.0750.0120.082-0.040-0.1920.0130.1710.1100.0820.701
LDL cholesterol (mg/dL)0.1210.160-0.0190.0390.1180.6310.4070.0950.0460.483-0.056-0.0531.000-0.0200.7010.1560.1330.038-0.247-0.5180.1940.0190.1410.000
Systolic blood pressure (mmHg)0.2180.2930.7940.017-0.0070.0090.0900.0470.023-0.016-0.021-0.026-0.0201.0000.0650.2620.3010.7040.0120.0350.0000.8110.2960.000
Total cholesterol (mg/dL)0.1630.1420.086-0.0600.2690.6010.683-0.028-0.0600.487-0.080-0.0750.7010.0651.0000.1280.1140.106-0.098-0.2070.0450.0680.1260.000
Waist circumference (cm)0.2900.8980.3160.186-0.0040.0840.1020.1730.1900.0230.0130.0120.1560.2620.1281.0000.8980.363-0.039-0.0570.0390.2700.8960.000
Weight (kg)0.2210.9380.3270.1430.0120.0890.0780.1450.1480.0350.0820.0820.1330.3010.1140.8981.0000.385-0.024-0.0720.0960.2981.0000.000
diastolic blood pressure0.3010.3870.8470.074-0.0230.0310.1140.0690.074-0.070-0.040-0.0400.0380.7040.1060.3630.3851.0000.0330.0340.0000.7920.3850.000
month0.002-0.0250.050-0.159-0.100-0.2140.0080.042-0.158-0.174-0.186-0.192-0.2470.012-0.098-0.039-0.0240.0331.0000.3570.993-0.022-0.0230.473
original_record_index0.014-0.0890.1100.0110.003-0.1970.0480.0230.008-0.4180.0270.013-0.5180.035-0.207-0.057-0.0720.0340.3571.0000.2770.029-0.0800.000
season0.0380.0000.0020.1860.1500.0940.1420.0530.1820.1510.1740.1710.1940.0000.0450.0390.0960.0000.9930.2771.0000.0000.1010.406
systolic blood pressure0.1510.2920.668-0.001-0.0230.0070.0870.021-0.001-0.0340.1100.1100.0190.8110.0680.2700.2980.792-0.0220.0290.0001.0000.2980.126
weight0.2180.9370.3230.1330.0210.0930.0840.1300.1330.0490.0820.0820.1410.2960.1260.8961.0000.385-0.023-0.0800.1010.2981.0000.000
year0.0940.0000.0000.1780.1580.1060.1000.0000.1860.0000.7010.7010.0000.0000.0000.0000.0000.0000.4730.0000.4060.1260.0001.000

Missing values

2025-11-11T12:41:30.247840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-11-11T12:41:34.754414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-11-11T12:41:40.574475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

anonymous_patient_idprimary_dateyearmonthseasonAge (at enrolment)systolic blood pressurediastolic blood pressureweightHeightPatient IDoriginal_record_indexdateFASTING GLUCOSEFASTING HDLFASTING LDLFASTING TOTAL CHOLESTEROLprimary_date_parsedHDL cholesterol (mg/dL)LDL cholesterol (mg/dL)Total cholesterol (mg/dL)BMI (kg/m²)Height (m)Weight (kg)Waist circumference (cm)Fasting glucose (mmol/L)Systolic blood pressure (mmHg)Diastolic blood pressure (mmHg)Fasting glucose (mg/dL)
221HEAT_0D62D894DA8A2011-02-1020112Summer19.4124.00000073.00000059.81.584WBS 0010.02011-02-105.031.231.412.772011-02-101.231.412.7724.21.58459.8830.05.03124.073.05.03
222HEAT_4D7DE69432FE2011-04-0920114Autumn39.4128.00000087.00000083.91.589WBS 0031.02011-04-094.550.901.544.932011-04-090.901.544.9333.61.58983.91030.04.55128.087.04.55
223HEAT_4D7DE69432FE2012-01-2120121SummerNaNNaNNaNNaNNaNWBS 0032.02012-01-214.761.332.205.112012-01-210.901.544.9333.1NaNNaNNaN4.76128.087.04.55
224HEAT_4D7DE69432FE2012-04-0220124Autumn40.0110.00000077.00000084.71.598WBS 0033.02012-04-026.721.612.375.352012-04-020.901.544.9333.51.59884.71020.06.72128.087.04.55
225HEAT_4D7DE69432FE2013-05-1620135Autumn42.0104.66666777.66666776.00.159WBS 0034.02013-05-165.681.713.365.892013-05-160.901.544.9330.10.15976.0890.05.68128.087.04.55
226HEAT_1B67A8E196F02011-03-1920113Autumn39.0112.00000076.00000068.01.762WBS 0045.02011-03-195.031.163.033.972011-03-191.163.033.9722.01.76268.0770.05.03112.076.05.03
227HEAT_1B67A8E196F02011-08-2720118Winter40.0NaNNaNNaNNaNWBS 0046.02011-08-274.320.522.482.522011-08-271.163.033.9721.5NaNNaNNaN4.32112.076.05.03
228HEAT_1B67A8E196F02012-02-0920122Summer40.0102.33333377.33333365.01.759WBS 0047.02012-02-095.480.952.714.172012-02-091.163.033.9721.21.75965.0770.05.48112.076.05.03
229HEAT_1B67A8E196F02013-05-0920135Autumn41.0138.00000097.66666766.10.175WBS 0048.02013-05-095.261.042.604.472013-05-091.163.033.9721.60.17566.1770.05.26112.076.05.03
230HEAT_42F4CBECF58F2011-03-1720113Autumn22.2111.00000078.00000051.81.646WBS 0059.02011-03-174.250.902.173.132011-03-170.902.173.1319.31.64651.8690.04.25111.078.04.25
anonymous_patient_idprimary_dateyearmonthseasonAge (at enrolment)systolic blood pressurediastolic blood pressureweightHeightPatient IDoriginal_record_indexdateFASTING GLUCOSEFASTING HDLFASTING LDLFASTING TOTAL CHOLESTEROLprimary_date_parsedHDL cholesterol (mg/dL)LDL cholesterol (mg/dL)Total cholesterol (mg/dL)BMI (kg/m²)Height (m)Weight (kg)Waist circumference (cm)Fasting glucose (mmol/L)Systolic blood pressure (mmHg)Diastolic blood pressure (mmHg)Fasting glucose (mg/dL)
995HEAT_0B3BD856B19E2012-01-2120121SummerNaNNaNNaNNaNNaNWBS 311774.02012-01-214.971.322.196.242012-01-211.141.574.9622.6NaNNaNNaN4.97106.072.04.76
996HEAT_0B3BD856B19E2012-05-1220125Autumn27.0122.00000078.00000054.61.525WBS 311775.02012-05-12NaN1.962.686.822012-05-121.141.574.9623.61.52554.6720.0NaN106.072.04.76
997HEAT_0B3BD856B19E2011-06-1120116Winter28.0NaNNaNNaNNaNWBS 311776.02011-06-114.951.422.925.282011-06-111.141.574.9621.51.53850.4680.04.76106.072.04.76
998HEAT_05B2FB1A51B92011-06-1120116Winter33.3141.000000103.00000077.81.554WBS 312777.02011-06-115.381.101.053.702011-06-111.101.053.7032.41.55477.8970.05.38141.0103.05.38
999HEAT_05B2FB1A51B92011-11-16201111Spring34.0NaNNaNNaNNaNWBS 312778.02011-11-165.501.401.725.322011-11-161.101.053.7034.4NaNNaNNaN5.50141.0103.05.38
1000HEAT_05B2FB1A51B92012-05-0220125Autumn34.0141.666667102.66666790.81.562WBS 312779.02012-05-025.991.761.324.112012-05-021.101.053.7037.31.56290.81155.05.99141.0103.05.38
1001HEAT_05B2FB1A51B92013-05-0820135Autumn35.0146.333333103.33333391.10.155WBS 312780.02013-05-086.110.421.352.352013-05-081.101.053.7037.90.15591.11030.06.11141.0103.05.38
1002HEAT_59DF578C032D2011-06-0720116Winter31.3123.00000086.00000084.61.630WBS 313781.02011-06-075.210.911.003.522011-06-070.911.003.5231.81.63084.61010.05.21123.086.05.21
1003HEAT_59DF578C032D2011-11-10201111Spring32.0NaNNaNNaNNaNWBS 313782.02011-11-104.671.020.592.932011-11-100.911.003.5231.2NaNNaNNaN4.67123.086.05.21
1004HEAT_59DF578C032D2012-05-0220125Autumn32.0125.00000093.33333387.21.627WBS 313783.02012-05-025.76NaNNaNNaN2012-05-020.911.003.5233.21.62787.21040.05.76123.086.05.21